John H. Duffus, Monica Nordberg, Douglas M. Templeton · IUPAC Standards Online · 2016
This glossary, a revision of the IUPAC "Glossary for Chemists of Terms Used in Toxicology" [Pure Appl. Chem.65, 2003 (1993)] incorporating new and redefined terms from the "Glossary of Terms Used in Toxicokinetics" [Pure Appl. Chem.76, 1033 (2004)], contains definitions and explanatory notes, if needed, for terms frequently used in the multidisciplinary field of toxicology. The glossary is compiled primarily for those scientists and others who now find themselves working in toxicology or requiring a knowledge of the subject, especially for hazard and risk assessment. Many medical terms are included because of their frequent occurrence in the toxicological literature. There are three annexes, one containing a list of abbreviations and acronyms used in toxicology, one containing a list of abbreviations and acronyms used by international bodies and by legislation relevant to toxicology and chemical safety, and one describing the classification of carcinogenicity according to the weight of evidence available.
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Veronica Ghini (1716565), Gaia Meoni (10063445), Lorenzo Pelagatti (12441581) et al. · Figshare · 2022
<p>A) Score plot of PCA analysis. Ellipsis indicate the 95% confidence intervals. B) Proximity plot of the RF model discriminating COVID-19≤21 patients and Post COVID-19 subjects with confusion matrix and accuracy value. In the confusion matrix, TP means true positive, FP false positive, TN true negative and FN false negative. Red dots: COVID-19≤21; black dots: COVID-19>21; blue dots: Post COVID-19.</p>
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Wighus, Stian Hammerseth · Duo Research Archive (University of Oslo) · 2023
This bachelor thesis provides an introduction to multivariate statistics, which is\nthe analysis of data with multiple variables using statistical methods. The thesis\nfocuses on the generalization of the normal distribution to random vectors, properties of the multivariate normal distribution, and non-parametric kernel estimation\nmethods for estimating densities. Additionally, the thesis presents methods for\nseparating populations and classifying new observations within these populations\nusing multivariate statistics. The applications of these methods is demonstrated\nusing a real data set, and the accuracy of the classification is evaluated.
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Yasunori Fujikoshi, Vladimir V. Ulyanov, Ryoichi Shimizu · Wiley series in probability and statistics · 2010
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Cindy L. Bethel, Kristen Salomon, Robin R. Murphy · 2015
This paper describes preliminary results of a large-scale, complex human study in HRI in which results show that participants were calmer interacting with non-anthropomorphic robots operated in an emotive mode versus a standard, non-emotive mode.
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Werner Dubitzky, Olaf Wolkenhauer, Kwang-Hyun Cho et al. · 2013
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2014
Data analysts are also interested in multivariate inferential methods, where the relationships between two variables, or between one target variable and a set of predictor variables, are analyzed. This chapter begins with bivariate analysis, where we have two independent samples and wish to test for significant differences in the means or proportions of the two samples. It focuses on a continuous variable, use the two-sample t-test for the difference in means. The chapter explains a flag variable, use the two-sample Z-test for the difference in proportions. It contains a multinomial variable, use the test for the homogeneity of proportions. The chapter deals with chi-square test, analysis of variance (ANOVA), and regression analysis. It focuses on confidence intervals for the mean value of y given x. Multiple regression models provide improved precision for estimation and prediction, analogous to the improved precision of regression estimates over univariate estimates.
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Jelena Marinković · 2008
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G. M. Vasques A, S. Grunwald A, N. B. Comerford B et al. · 2009
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Debbie L. Hahs‐Vaughn · 2024
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2008
Citation: 'multivariate statistics' in the IUPAC Compendium of Chemical Terminology, 3rd ed.; International Union of Pure and Applied Chemistry; 2006. Online version 3.0.1, 2019. 10.1351/goldbook.MT06974 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.
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2012
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Martin H. Trauth · Springer textbooks in earth sciences, geography and environment · 2025
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2008
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Martin H. Trauth · 2007
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Matthias Plaue · Data Science · 2023
When we learned about association measures and regression methods in the previous chapters, we gained a first glimpse into multivariate statistics. Multivariate methods allow for the joint study of statistical variables and their relationships between each other with the goal of capturing a complete picture of the data. In machine learning, this approach is essential, as predictions are based on a large number of features and their statistical characteristics.
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Martin H. Trauth · Springer textbooks in earth sciences, geography and environment · 2024
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Elinor Jones, Simon Harden, Michael J. Crawley · 2022
This chapter covers the following subjects: visualising data; multivariate analysis of variance; and principal component analysis to analyse the main sources of variation in the data. It also explains the factor analysis, cluster analysis, discriminant analysis and the neural networks. Beginners are sometimes attracted to multivariate techniques because of the complexity of the outputs they produce. These techniques (with the exception of discriminant analysis and neural networks) are also described as unsupervised learning, i.e. they attempt to find patterns in data without any guidance as to what these patterns might be. Multivariate data often exists in high dimensions, and so it can be difficult to produce helpful summary statistics and plots. A good starting point is appropriate univariate plots for each variable (barchart, histogram, or boxplot for instance) and bivariate scatter plots between pairs.
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2008
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Wolfgang Karl Härdle, Zdeněk Hlávka · 2015
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